class_docstr: Unicode script tokenization layer.
  
  Splits a string when successive tokens change their Unicode script or change
  being whitespace or not. By not keeping the whitespace tokens, this allows
  you to split on whitespace, and also to split out tokens from different
  scripts (so, for instance, a string with both Latin and Japanese characters
  would be split at the boundary of the Latin and Japanese characters in
  addition to any whitespace boundaries).
  
  Attributes:
    keep_whitespace: A boolean that specifices whether to emit whitespace
      tokens (default `False`).
    pad_value: if not None, performs the padding (using pad_value) at the
      inner-most dimension (i.e. token dimension) and outputs a padded dense
      tensor (default=None).
    squeeze_token_dim: Whether to squeeze the dimension added by tokenization.
      When this arg is set to False, the output will have an additional inner
      dimension added, containing the tokens in each string; when this arg is
      True, the layer will attempt to squeeze that dimension out. If you are
      passing one string per batch, you probably want to keep this as True; if
      you are passing more than one string per batch or are using this layer in
      a context like the Keras `TextVectorization` layer which expects a
      tf.strings.split()-stype output, this should be False. Defaults to True.
code: def __init__(
      self,
      keep_whitespace=False,
      pad_value=None,
      squeeze_token_dim=True,
      **kwargs,
  ):
      tokenizer_fn = unicode_script_tokenizer.UnicodeScriptTokenizer(
          keep_whitespace=keep_whitespace
      )
      super(UnicodeScriptTokenizer, self).__init__(
          tokenizer_instance=tokenizer_fn,
          squeeze_token_dim=squeeze_token_dim,
          pad_value=pad_value,
          **kwargs,
      )
  
imports: import os
  import tensorflow
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.ops.ragged import ragged_conversion_ops
  from tensorflow_text.python.ops import unicode_script_tokenizer
  from tensorflow_text.python.ops import whitespace_tokenizer
  from tensorflow_text.python.ops import wordpiece_tokenizer
label_desc: Use image node_id 1 to create a new UnicodeScriptTokenizer object from inherited base classes: TokenizerBase with example: obj = UnicodeScriptTokenizer(keep_whitespace, pad_value, squeeze_token_dim)